IEEE Transactions on Cloud Computinghttp://www.computer.org
en-usMon, 3 Nov 2014 15:35:55 GMThttp://csdl.computer.org/common/images/logos/tcc.gifIEEE Computer SocietyList of recently published journal articleshttp://www.computer.org/tcc
PrePrint: A Scientometric Analysis of Cloud Computing Literaturehttp://doi.ieeecomputersociety.org/10.1109/TCC.2014.2321168
The popularity and rapid development of cloud computing in recent years has led to a huge amount of publications containing the achieved knowledge of this area of research. Due to the interdisciplinary nature and high relevance of cloud computing research, it becomes increasingly difficult or even impossible to understand the overall structure and development of this field without analytical approaches. While evaluating science has a long tradition in many fields, we identify a lack of a comprehensive scientometric study in the area of cloud computing. Based on a large bibliographic data base, this study applies scientometric means to empirically study the evolution and state of cloud computing research with a view from above the clouds. By this, we provide extensive insights into publication patterns, research impact and research productivity. Furthermore, we explore the interplay of related subtopics by analyzing keyword clusters. The results of this study provide a better understanding of patterns, trends and other important factors as a basis for directing research activities, sharing knowledge and collaborating in the area of cloud computing research.http://doi.ieeecomputersociety.org/10.1109/TCC.2014.2321168PrePrint: Workload Prediction Using ARIMA Model and Its Impact on Cloud Applications' QoShttp://doi.ieeecomputersociety.org/10.1109/TCC.2014.2350475
As companies shift from desktop applications to Cloud-based Software as a Service (SaaS) applications deployed on public Clouds, the competition for end-users by Cloud providers offering similar services grows. In order to survive in such a competitive market, Cloud-based companies must achieve good Quality of Service (QoS) for their users, or risk losing their customers to competitors. However, meeting the QoS with a cost-effective amount of resources is challenging because workloads experience variation over time. This problem can be solved with proactive dynamic provisioning of resources, which can estimate the future need of applications in terms of resources and allocate them in advance, releasing them once they are not required. In this paper, we present the realization of a Cloud workload prediction module for SaaS providers based on the Autoregressive Integrated Moving Average (ARIMA) model. We introduce the prediction based on the ARIMA model and evaluate its accuracy of future workload prediction using real traces of requests to web servers.We also evaluate the impact of the achieved accuracy in terms of efficiency in resource utilization and QoS. Simulation results show that our model is able to achieve an average accuracy of up to 91%, which leads to efficiency in resource utilization with minimal impact on the QoS.http://doi.ieeecomputersociety.org/10.1109/TCC.2014.2350475PrePrint: An Efficient Green Control Algorithm in Cloud Computing for Cost Optimizationhttp://doi.ieeecomputersociety.org/10.1109/TCC.2014.2350492
Cloud computing is a new paradigm for hosting and delivering remote computing resources through a network. However, achieving an energy-efficiency control and simultaneously satisfying a performance guarantee have become critical issues for cloud providers. In this paper, three power-saving policies are implemented in cloud systems to mitigate server idle power. The challenges of controlling service rates and applying the N-policy to optimize operational cost within a performance guarantee are first studied. A cost function has been developed in which the operational costs of power consumption, system congestion and mode-switching are all taken into consideration. The effect of energy-efficiency controls on response times, operating modes and incurred costs are demonstrated and compared. Our objectives are to find the optimal service rate and mode-switching restriction, so as to minimize cost within a response time guarantee under varying arrival rates. An efficient green control (EGC) algorithm is first proposed for solving constrained optimization problems and making costs/performances tradeoffs in systems with different power-saving policies. Simulation results show that the benefits of reducing operational costs and improving response times can be verified by applying the power-saving policies combined with the proposed algorithm as compared to a typical system with a same performance guarantee.http://doi.ieeecomputersociety.org/10.1109/TCC.2014.2350492PrePrint: Adaptive Workflow Scheduling on Cloud Computing Platforms with Iterative Ordinal Optimizationhttp://doi.ieeecomputersociety.org/10.1109/TCC.2014.2350490
The scheduling of multitask jobs on clouds is an NP-hard problem. The problem becomes even worse when complex workflows are executed on elastic clouds, such as Amazon EC2 or IBM RC2. The main difficulty lies in the large search space and high overhead of generating optimal schedules, especially for real-time applications with dynamic workloads. In this work, a new iterative ordinal optimization (IOO) method is proposed. The ordinal optimization method is applied in each iteration to achieve suboptimal schedules. IOO aims at generating more efficient schedules from a global perspective over a long period. We prove through overhead analysis the advantages in time and space efficiency in using the IOO method. The IOO method is designed to adapt to system dynamism to yield suboptimal performance. In cloud experiments on IBM RC2 cloud, we execute 20,000 tasks in LIGO (Laser Interferometer Gravitational-wave Observatory) verification workflow on 128 virtual machines. The IOO schedule is generated in less than 1,000 seconds, while using the Monte Carlo simulation takes 27.6 hours, 100 times longer to yield an optimal schedule. The IOO-optimized schedule results in a throughput of 1,100 tasks/sec with 7 GB memory demand, compared with 60% decrease in throughput and 70% increase in memory demand in using the Monte Carlo method. Our LIGO experimental results clearly demonstrate the advantage of using the IOO-based workflow scheduling over the traditional blind-pick, ordinal optimization, or Monte Carlo methods. These numerical results are also validated by the theoretical complexity and overhead analysis provided.http://doi.ieeecomputersociety.org/10.1109/TCC.2014.2350490PrePrint: DynamicMR: A Dynamic Slot Allocation Optimization Framework for MapReduce Clustershttp://doi.ieeecomputersociety.org/10.1109/TCC.2014.2329299
MapReduce is a popular computing paradigm for large-scale data processing in cloud computing. However, the slot-based MapReduce system (e.g., Hadoop MRv1) can suffer from poor performance due to its unoptimized resource allocation. To address it, this paper identifies and optimizes the resource allocation from three key aspects. First, due to the pre-configuration of distinct map slots and reduce slots which are not fungible, slots can be severely under-utilized. Because map slots might be fully utilized while reduce slots are empty, and vice-versa. We proposes an alternative technique called Dynamic Hadoop Slot Allocation by keeping the slot-based model. It relaxes the slot allocation constraint to allow slots to be reallocated to either map or reduce tasks depending on their needs. Second, the speculative execution can tackle the straggler problem, which has shown to improve the performance for a single job but at the expense of the cluster efficiency. In view of this, we propose Speculative Execution Performance Balancing to balance the performance tradeoff between a single job and a batch of jobs. Third, delay scheduling has shown to improve the data locality but at the cost of fairness. Alternatively, we propose a technique called Slot PreScheduling that can improve the data locality but with no impact on fairness. Finally, by combining these techniques together, we form a step-by-step slot allocation system called DynamicMR that can improve the performance of MapReduce workloads substantially. The experimental results show that our DynamicMR can improve the performance of Hadoop MRv1 significantly while maintaining the fairness, by up to 46% 115% for single jobs and 49% 112% for multiple jobs. Moreover, we make a comparison with YARN experimentally, showing that DynamicMR outperforms YARN by about 2% 9% for multiple jobs due to its ratio control mechanism of running map/reduce tasks.http://doi.ieeecomputersociety.org/10.1109/TCC.2014.2329299PrePrint: A Scalable and Reliable Matching Service for Content-basedPublish/Subscribe Systemshttp://doi.ieeecomputersociety.org/10.1109/TCC.2014.2338327
Characterized by the increasing arrival rate of live content, the emergency applications pose a great challenge: how to disseminate large-scale live content to interested users in a scalable and reliable manner. The publish/subscribe (pub/sub) model is widely used for data dissemination because of its capacity of seamlessly expanding the system to massive size. However, most event matching services of existing pub/sub systems either lead to low matching throughput when matching a large number of skewed subscriptions, or interrupt dissemination when a large number of servers fail. The cloud computing provides great opportunities for the requirements of complex computing and reliable communication. In this paper, we propose SREM, a scalable and reliable event matching service for content-based pub/sub systems in cloud computing environment. To achieve low routing latency and reliable links among servers, we propose a distributed overlay SkipCloud to organize servers of SREM. Through a hybrid space partitioning technique HPartition, large-scale skewed subscriptions are mapped into multiple subspaces, which ensures high matching throughput and provides multiple candidate servers for each event. Moreover, a series of dynamics maintenance mechanisms are extensively studied. To evaluate the performance of SREM, 64 servers are deployed and millions of live content items are tested in a CloudStack testbed. Under various parameter settings, the experimental results demonstrate that the traffic overhead of routing events in SkipCloud is at least 60% smaller than in Chord overlay, the matching rate in SREM is at least 3.7 times and at most 40.4 times larger than the single-dimensional partitioning technique of BlueDove. Besides, SREM enables the event loss rate to drop back to 0 in tens of seconds even if a large number of servers fail simultaneously.http://doi.ieeecomputersociety.org/10.1109/TCC.2014.2338327PrePrint: Privacy-Preserving Data Storage in Cloud Using Array BP-XOR Codeshttp://doi.ieeecomputersociety.org/10.1109/TCC.2014.2344662
LDPC codes, LT codes, and digital fountain techniques have received significant attention from both academics and industry in the past few years. By employing the underlying ideas of efficient Belief Propagation (BP) decoding process in LDPC and LT codes, this paper designs the BP-XOR codes and use them to design three classes of secret sharing schemes called BP-XOR secret sharing schemes, pseudo-BP-XOR secret sharing schemes, and LDPC secret sharing schemes. By establishing the equivalence between the edge-colored graph model and degreetwo BP-XOR secret sharing schemes, we are able to design novel perfect and ideal 2-out-of-n BP-XOR secret sharing schemes. By employing techniques from array code design, we are also able to design other (n; k) threshold LDPC secret sharing schemes. In the efficient (pseudo) BP-XOR/LDPC secret sharing schemes that we will construct, only linear number of XOR (exclusive-or) operations on binary strings are required for both secret distribution phase and secret reconstruction phase. For a comparison, we should note that Shamir secret sharing schemes require O(n log n) field operations for the secret distribution phase and O(n2) field operations for the secret reconstruction phase. Furthermore, our schemes achieve the optimal update complexity for secret sharing schemes. By update complexity for a secret sharing scheme, we mean the average number of bits in the participant’s shares that needs to be revised when certain bit of the master secret is changed. The extremely efficient secret sharing schemes discussed in this paper could be used for massive data storage in cloud environments achieving privacy and reliability without employing encryption techniques.http://doi.ieeecomputersociety.org/10.1109/TCC.2014.2344662PrePrint: LsPS: A Job Size-Based Scheduler for Efficient Assignments in Hadoophttp://doi.ieeecomputersociety.org/10.1109/TCC.2014.2338291
The MapReduce paradigm and its open source implementation Hadoop are emerging as an important standard for largescale data-intensive processing in both industry and academia. A MapReduce cluster is typically shared among multiple users with different types of workloads. When a flock of jobs are concurrently submitted to a MapReduce cluster, they compete for the shared resources and the overall system performance in terms of job response times, might be seriously degraded. Therefore, one challenging issue is the ability of efficient scheduling in such a shared MapReduce environment. However, we find that conventional scheduling algorithms supported by Hadoop cannot always guarantee good average response times under different workloads. To address this issue, we propose a new Hadoop scheduler, which leverages the knowledge of workload patterns to reduce average job response times by dynamically tuning the resource shares among users and the scheduling algorithms for each user. Both simulation and real experimental results from Amazon EC2 cluster show that our scheduler reduces the average MapReduce job response time under a variety of system workloads compared to the existing FIFO and Fair schedulers.http://doi.ieeecomputersociety.org/10.1109/TCC.2014.2338291PrePrint: FastRAQ: A Fast Approach to Range-Aggregate Queries in Big Data Environmentshttp://doi.ieeecomputersociety.org/10.1109/TCC.2014.2338325
Range-aggregate queries are to apply a certain aggregate function on all tuples within given query ranges. Existing approaches to range-aggregate queries are insufficient to quickly provide accurate results in big data environments. In this paper, we propose FastRAQ — a fast approach to range-aggregate queries in big data environments. FastRAQ first divides big data into independent partitions with a balanced partitioning algorithm, and then generates a local estimation sketch for each partition. When a range-aggregate query request arrives, FastRAQ obtains the result directly by summarizing local estimates from all partitions. FastRAQ has O(1) time complexity for data updates and O( N PB ) time complexity for range-aggregate queries, where N is the number of distinct tuples for all dimensions, P is the partition number, and B is the bucket number in the histogram. We implement the FastRAQ approach on the Linux platform, and evaluate its performance with about ten billions data records. Experimental results demonstrate that FastRAQ provides range-aggregate query results within a time period two orders of magnitude lower than that of Hive, while the relative error is less than 3% within the given confidence interval.http://doi.ieeecomputersociety.org/10.1109/TCC.2014.2338325PrePrint: Towards Optimized Fine-Grained Pricing of IaaS Cloud Platformhttp://doi.ieeecomputersociety.org/10.1109/TCC.2014.2344680
Although many pricing schemes in IaaS platform are already proposed with pay-as-you-go and subscription/spot market policy to guarantee service level agreement, it is still inevitable to suffer from wasteful payment because of coarsegrained pricing scheme. In this paper, we investigate an optimized fine-grained and fair pricing scheme. Two tough issues are addressed: (1) the profits of resource providers and customers often contradict mutually; (2) VM-maintenance overhead like startup cost is often too huge to be neglected. Not only can we derive an optimal price in the acceptable price range that satisfies both customers and providers simultaneously, but we also find a best-fit billing cycle to maximize social welfare (i.e., the sum of the cost reductions for all customers and the revenue gained by the provider). We carefully evaluate the proposed optimized fine-grained pricing scheme with two large-scale real-world production traces (one from Grid Workload Archive and the other from Google data center). We compare the new scheme to classic coarse-grained hourly pricing scheme in experiments and find that customers and providers can both benefit from our new approach. The maximum social welfare can be increased up to 72:98% and 48:15% with respect to DAS-2 trace and Google trace respectively.http://doi.ieeecomputersociety.org/10.1109/TCC.2014.2344680